BIA – Chapter 9: Business Intelligence Systems – Flashcards
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How do organizations use business intelligence (BI) systems?
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Organizations use BI for all four of the collaborative tasks. BI is used just for informing. Medical staff can use PRIDE to learn how patients are using the new system. At the time of the analysis, the staff may not have any particular purpose in mind, but just are browsing the BI results for some future, unspecified purpose.
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business intelligence (BI) systems
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are information systems that process operational and other data to analyze past performance and to make predictions.
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business intelligence
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the patterns, relationships, and trends identified by BI systems.
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what are typical uses for BI?
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- identifying changes in purchasing patterns - BI for Entertainment - Predictive Policing
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Changes in Purchasing Patterns
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important life events cause customers to change what they buy ex: the most common time when women give birth/get preggo --> sell diapers, baby products, etc. by delivering ads and magazines
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BI for Entertainment
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Netflix has data on watching, listening, and rental habits, however, determines what people actually want, not what they say. Another example is when you buy products on amazon and it suggests to you later on the side what other products you might like to buy based on past purchases.
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predictive policing
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police departments analyze data on past crimes, including location, date, time, day of week, type of crime ad related data to predict where crimes are likely to occur.
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BI application
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as information systems, BI systems have the five standard components: hardware, software, data, procedures, and people. software component of a BI system are called a BI application.
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some managers use BI systems for decision making:
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EXAMPLE Dr. Flores is concerned that some patients may be exercising too much; he can use BI to determine if anyone is and, if so, who they are.
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decision support systems
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as a synonym for decision-making BI systems
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What are the three primary activities in the BI process?
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1. acquire data 2. perform analysis 3. publish results
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acquire data
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- obtain - cleanse - organize & relate - catalog
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analyze data
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- reporting - data mining - BigData (combine the data in the two tables into a single table that contained both the sales and part data)
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publish results
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- print - web servers - report servers - automation (is the last activity in the BI process. It places BI results on servers for publication to knowledge workers over the Internet or other networks) (in other cases, it means making the results available via a Web service for use by other applications) (it also means creating PDF's or PowerPoint presentations for communicating to colleagues or management)
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data acquisition
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the process of obtaining, cleaning, organizing, relating and cataloging source data
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BI analysis
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the process of creating business intelligence.
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the three fundamental categories of BI analysis:
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1. reporting 2. data mining 3. BigData
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publish results
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the process of delivering business intelligence to the knowledge workers who need it - push publishing - pull publishing
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push publishing
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delivers business intelligence to users without any request from users; the BI results are delivered according to a schedule or as a result of an event or particular data condition. - like emails sent to you because you subscribed
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pull publishing
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requires the user to request BI results publishing media include print as well as online content delivered via Web servers, specialized Web servers known as report servers, and BI results that are sent via automation to other programs. PG. 252 CHART REFERENCE!!!
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How do organizations use data warehouses and data marts to acquire data?
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Most organizations extract operational data for BI processing. (for a small organization, the extraction may be as simple as an Access Database) (larger organizations, however, typically create and staff a group of people who manage and run a data warehouse)
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data warehouse
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the facility for managing an organizations BI data. It includes data that are purchased from outside sources. the prepared data are stored in a data warehouse database using a data warehouse DBMS, which can be different from the organization's operational DBMS ex: an organization might use Oracle for its operational processing but use SQL Server for its data warehouse. other organizations use SQL Server for operational processing but use DBMSs from statistical package vendors such as SAS or SPSS in the data warehouse.
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functions of a data warehouse:
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extract data from operational, internal and external databases cleanse data organize, relate data warehouse catalog data using metadata
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problems with source data:
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- although data that are critical for successful operations must be complete and accurate, data that are only marginally necessary need not be. - dirty data aka problematic data - missing values - inconsistent data - data not integrated - wrong granularity (too fine, or not fine enough) - too much data (too many attributes, too many data points)
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inconsistent data -
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ex: the phone number for a given customer before the change will not match the customer's number after the change. part codes can change, as can sales territories.
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nonintegrated data -
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ex: a particular BI system might require data from an ERP system, an e-commerce system, and a social networking application. Analysts may wish to integrate that organizational data with purchased consumer data. Such a data collection will likely have relationships that are not represented in primary key/foreign key relationships. It is the function of personnel in the data warehouse to integrate such data somehow.
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granularity
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refers to level of detail represented by the data it can be too fine or too coarse. - ex: clickstream data!! (too fine)--> If the clickstream data shows ALL/EVERYTHINGGG the customer does at the Web site, all of that data may be useful for a study of consumer browsing behavior - but it will be overwhelming if all we want is to know how customers respond to an ad located differently on the screen. - ex: too coarse data --> a file of regional sales totals cannot be used to investigate the sales in a particular store in a region, and total sales for a store cannot be used to determine the sales of particular items within a store. Instead, we need to obtain data that is fine enough for the lowest-level report we want to produce. **it is better to have too fine a granularity than too coarse.
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curse of dimensionality
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the more attributes there are, the easier it is to build a model that fits the sample data, but that is worthless as a predictor.
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too many data points -
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too many rows of data = NOT HELPFUL! In order to meaningfully analyze such data, we need to reduce the amount of data! One good solution to this problem is statistical sampling.
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data mart
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data collection, smaller than a data warehouse, that addresses the needs of a particular department of functional area of the business
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functions of data mart:
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it's like a retail store in a supply chain. users in the data mart obtain data that pertain to a particular business function from the data warehouse. such users do not have the data management expertise that data warehouse employees have, but they are knowledgeable analysts for a given business function.
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components of a data warehouse with a data mart:
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data warehouse metadata data warehouse database / DBMS DATA MART COMPONENTS: web log data - BI tools for web clickstream analysis store log data - BI tools for store mgt inventory history data - BI tools for inventory mgt
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What are the three techniques for processing BI data?
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- reporting analysis - data mining analysis - BigData analysis
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reporting analysis
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to process structured data by: 1. Sorting 2. Filtering 3. Grouping 4. Calculating 5. Formatting create information about past performance.
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structured data
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data in the form of rows and columns. most of the time, structured data means tables in a relational database, but it can refer to spreadsheet data as well.
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exception reports
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are reports produced when something out of predefined bounds occurs. ex: a report that is produced when the Dow Jones falls below a certain level is an exception report.
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data mining
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to classify and predict. 1. unsupervised data mining - cluster analysis 3. supervised data mining - regression analysis application of sophisticated statistical techniques to find patterns and relationships among data for classification and prediction Data mining techniques emerged from the combined discipline of statistics, mathematics, artificial intelligence, and machine-learning.
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unsupervised data mining
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analysts do not create a model of hypothesis before running the analysis. Instead, they apply the data mining technique to the data and observe the results. with this method, analysts create hypotheses after the analysis to explain the patterns found.
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cluster analysis
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statistical techniques identify groups of entities that have similar characteristics a common use of cluster analysis is to find groups of similar customers from customer order and demographic data.
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supervised data mining
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data miners develop a model PRIOR to the analysis and apply statistical techniques to data to estimate parameters of the model
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regression analysis
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measures of impact of a set of variables on another variable
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BigData
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to find patterns and relationships in BigData. term used to describe data collections that are characteristic by huge volume, rapid velocity and great variety [VVV] BigData refers to data sets that are at least a petabyte in size, and usually larger. Volume, velocity, and variety force use of MapReduce techniques. Some applications use reporting and data mining as well. - free-form text - different formats of web server and database log files - streams of data about user responses to page content; graphics, audio and video files
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MapReduce
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is a technique for harnessing the power of thousands of computers working in parallel. the basic idea is that the BigData collection is broken into pieces for something of interest. That process is referred to as the MAP phase. ex: a data set having the logs of Google searches is broken into pieces, and each independent processor is instructed to search for and count search keywords. As the processors finish, their results are combined in what is referred to as the REDUCE phase. The result is a list of all the terms searched for on a given day and the count of each.
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Hadoop
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is an open source program supported by the Apache Foundation2 that manages thousands of computers and that implements MapReduce. Hadoop includes a query language entitled Pig. Amazon.com supports Hadoop as part of EC3 cloud offering. Hadoop could drive the process of finding and counting the Google search terms. Hadoop is written in Java and originally ran on Linux; Microsoft has implemented it on Windows Server as well. Deep technical skills are needed to run and use Hadoop.
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What are the alternative for publishing BI?
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- static reports - dynamic reports - subscriptions
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static reports
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BI documents that are FIXED at the time of creation and do not change ex: printed sales analysis
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dynamic reports
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BI documents that are updated at the time they are requested/accessed (like VARIABLE) ex: a sales report that is current as of the time that the user accessed it on a Web server is a dynamic report.
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subscriptions
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user requests for a particular schedule or in response to particular events
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examples of alternatives
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email --> static web server --> static, dynamic SharePoint --> static, dynamic BI Server --> dynamic
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BI server
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a web server application that is purpose - built for the publishing of business intelligence. ex: Microsoft SQL Server Report Manager BI servers use metadata to determine what results to send to which users and, possibly, on which schedule. Today, the expectation is that BI results can be delivered to "any" device.
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two functions of BI servers:
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management and delivery
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management function -
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maintains metadata about the authorized allocation of BI results to users. the BI server tracks what results are available, what users are authorized to view those results, and the schedule upon which the results are provided to the authorized users. It adjusts allocations as available results change and users come and go.
